New Evidence on the Properties of the Financial Statement Information Pricing Process

This study contributes to our knowledge of how information contained in financial statements is interpreted and priced by the stock market in two aspects. First, the empirical findings indicate that investors interpret some of the information contained in new financial statements in the context of the information of prior financial statements. Second, two central hypotheses offered in earlier literature to explain the significant connection between publicly available financial statement information and future abnormal returns, that the signals proxy for risk and that the information is priced with a delay, are evaluated utilizing a new methodology. It is found that the mentioned significant connection for some financial statement signals can be explained by that the signals proxy for risk and for other financial statement signals by that the information contained in the signals is priced with a delay.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Essays on Corporate Governance and the Quality of Disclosed Earnings: Across Transitional Europe

A growing body of empirical research examines the structure and effectiveness of corporate governance systems around the world. An important insight from this literature is that corporate governance mechanisms address the excessive use of managerial discretionary powers to get private benefits by expropriating the value of shareholders. One possible way of expropriation is to reduce the quality of disclosed earnings by manipulating the financial statements. This lower quality of earnings should then be reflected by the stock price of firm according to value relevance theorem. Hence, instead of testing the direct effect of corporate governance on the firm’s market value, it is important to understand the causes of the lower quality of accounting earnings. This thesis contributes to the literature by increasing knowledge about the extent of the earnings management – measured as the extent of discretionary accruals in total disclosed earnings - and its determinants across the Transitional European countries. The thesis comprises of three essays of empirical analysis of which first two utilize the data of Russian listed firms whereas the third essay uses data from 10 European economies. More specifically, the first essay adds to existing research connecting earnings management to corporate governance. It testifies the impact of the Russian corporate governance reforms of 2002 on the quality of disclosed earnings in all publicly listed firms. This essay provides empirical evidence of the fact that the desired impact of reforms is not fully substantiated in Russia without proper enforcement. Instead, firm-level factors such as long-term capital investments and compliance with International financial reporting standards (IFRS) determine the quality of the earnings. The result presented in the essay support the notion proposed by Leuz et al. (2003) that the reforms aimed to bring transparency do not correspond to desired results in economies where investor protection is lower and legal enforcement is weak. The second essay focuses on the relationship between the internal-control mechanism such as the types and levels of ownership and the quality of disclosed earnings in Russia. The empirical analysis shows that the controlling shareholders in Russia use their powers to manipulate the reported performance in order to get private benefits of control. Comparatively, firms owned by the State have significantly better quality of disclosed earnings than other controllers such as oligarchs and foreign corporations. Interestingly, market performance of firms controlled by either State or oligarchs is better than widely held firms. The third essay provides useful evidence on the fact that both ownership structures and economic characteristics are important factors in determining the quality of disclosed earnings in three groups of countries in Europe. Evidence suggests that ownership structure is a more important determinant in developed and transparent countries, while economic determinants are important determinants in developing and transitional countries.